学科方向Fields

按学科了解 AI 的应用和学习起点。 AI applications and starting points, by subject.

计算机与人工智能Computer Science & AI

算法、模型与系统，是 AI 的基础。Algorithms, models, and systems — the foundation of AI.

推荐起点Starting points

学好 Python，加一门数据结构课Learn Python well, plus a data structures course
再修一门机器学习导论课程Then take an intro machine learning course

数学与统计Mathematics & Statistics

线性代数、概率统计与优化，是机器学习的数学基础。Linear algebra, probability, and optimization are the math behind machine learning.

推荐起点Starting points

优先学线性代数与概率论Prioritize linear algebra and probability
结合机器学习中的实例来学Learn them through machine learning examples

自然科学Natural Sciences

AI 在科研中的应用：蛋白质结构预测、材料发现、气候模拟等。AI in scientific research: protein structure prediction, materials discovery, climate modeling.

推荐起点Starting points

先掌握机器学习的基本概念Get the machine learning basics first
研究本领域的经典案例（如 AlphaFold）Study landmark cases in your field (e.g. AlphaFold)

工程与应用Engineering & Applications

AI 在工程中的应用：机器人、自动驾驶、智能制造等。AI in engineering: robotics, autonomous driving, smart manufacturing.

推荐起点Starting points

控制与优化基础，加一门机器学习入门课Basics of control and optimization, plus an intro ML course
尽早做一个小的实践项目Build one small hands-on project early

人文与社会科学Humanities & Social Sciences

AI 在人文领域的应用：文本分析、翻译、数字人文，以及 AI 伦理。AI in the humanities: text analysis, translation, digital humanities, and AI ethics.

推荐起点Starting points

从用 AI 读文献、做文本分析入手Start with AI-assisted reading and text analysis
同时关注 AI 伦理方向的公开课程Follow open courses on AI ethics alongside

经济与商业Economics & Business

AI 在商业中的应用：预测、决策与自动化。AI in business: forecasting, decision-making, and automation.

推荐起点Starting points

先打好数据分析基础Data analysis basics first
多看真实的行业案例Look at real industry cases